# Trust in AI and Its Role in the Acceptance of AI Technologies arXiv: https://arxiv.org/abs/2203.12687 ยท HTML: https://mlsanity.com/paper/2203.12687/ ## Key facts - Cited by 29 papers in the map (data through 14 Oct 2026). - Builds on 5 of its 75 references that are in the map. - First posted on arXiv 23 Mar 2022. ## Builds on (most cited first, 5 of 5) - [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019) - [The Ethics of AI Ethics -- An Evaluation of Guidelines](https://mlsanity.com/paper/1903.03425/) (2019) - [Artificial Intelligence: the global landscape of ethics guidelines](https://mlsanity.com/paper/1906.11668/) (2019) - [Defining AI in Policy versus Practice](https://mlsanity.com/paper/1912.11095/) (2019) - [Trust as indicator of robot functional and social acceptance. An experimental study on user conformation to the iCub's answers](https://mlsanity.com/paper/1510.03678/) (2015) ## Built on by (most cited first, 29 of 29) - [MIRACLE: Towards Personalized Dialogue Generation with Latent-Space Multiple Personal Attribute Control](https://mlsanity.com/paper/2310.18342/) (2023) - [Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations](https://mlsanity.com/paper/2407.08983/) (2024) - [Anthropomorphization of AI: Opportunities and Risks](https://mlsanity.com/paper/2305.14784/) (2023) - [Learning to Prompt in the Classroom to Understand AI Limits: A pilot study](https://mlsanity.com/paper/2307.01540/) (2023) - [Interactive Explanation with Varying Level of Details in an Explainable Scientific Literature Recommender System](https://mlsanity.com/paper/2306.05809/) (2023) - [Explainability Paths for Sustained Artistic Practice with AI](https://mlsanity.com/paper/2407.15216/) (2024) - [Artificial Intelligence for Collective Intelligence: A National-Scale Research Strategy](https://mlsanity.com/paper/2411.06211/) (2024) - [Understanding Human-AI Trust in Education](https://mlsanity.com/paper/2506.09160/) (2025) - [LLM-D12: A Dual-Dimensional Scale of Instrumental and Relational Dependencies on Large Language Models](https://mlsanity.com/paper/2506.06874/) (2025) - [A multilevel framework for AI governance](https://mlsanity.com/paper/2307.03198/) (2023) - [Can I say, now machines can think?](https://mlsanity.com/paper/2307.07526/) (2023) - [Local Explanations and Self-Explanations for Assessing Faithfulness in black-box LLMs](https://mlsanity.com/paper/2409.13764/) (2024) - [Human Bias in the Face of AI: Examining Human Judgment Against Text Labeled as AI Generated](https://mlsanity.com/paper/2410.03723/) (2024) - [A closer look at how large language models trust humans: patterns and biases](https://mlsanity.com/paper/2504.15801/) (2025) - [Probing the Gaps in ChatGPT Live Video Chat for Real-World Assistance for People who are Blind or Visually Impaired](https://mlsanity.com/paper/2508.03651/) (2025) - [Revisiting Trust in the Era of Generative AI: Factorial Structure and Latent Profiles](https://mlsanity.com/paper/2510.10199/) (2025) - [Requirements for Explainability and Acceptance of Artificial Intelligence in Collaborative Work](https://mlsanity.com/paper/2306.15394/) (2023) - [From Explainability to Action: A Generative Operational Framework for Integrating XAI in Clinical Mental Health Screening](https://mlsanity.com/paper/2510.13828/) (2025) - [To Build or Not to Build? Factors that Lead to Non-Development or Abandonment of AI Systems](https://mlsanity.com/paper/2604.28053/) (2026) - [The impact of labeling automotive AI as "trustworthy" or "reliable" on user evaluation and technology acceptance](https://mlsanity.com/paper/2408.10905/) (2024) - [Perceptions of Discriminatory Decisions of Artificial Intelligence: Unpacking the Role of Individual Characteristics](https://mlsanity.com/paper/2410.13250/) (2024) - [The Impact of Transparency in AI Systems on Users' Data-Sharing Intentions: A Scenario-Based Experiment](https://mlsanity.com/paper/2502.20243/) (2025) - [Mapping the Trust Terrain: LLMs in Software Engineering -- Insights and Perspectives](https://mlsanity.com/paper/2503.13793/) (2025) - [Evaluation and Incident Prevention in an Enterprise AI Assistant](https://mlsanity.com/paper/2504.13924/) (2025) - [Emotionally Vulnerable Subtype of Internet Gaming Disorder: Measuring and Exploring the Pathology of Problematic Generative AI Use](https://mlsanity.com/paper/2510.06908/) (2025) - [Simulating Teams with LLM Agents: Interactive 2D Environments for Studying Human-AI Dynamics](https://mlsanity.com/paper/2510.08242/) (2025) - [Trust the AI, Doubt Yourself: The Effect of Urgency on Self-Confidence in Human-AI Interaction](https://mlsanity.com/paper/2604.07535/) (2026) - [Beyond Adoption Intention How Trust in Augmented Analytics Relates to Perceived Decision Quality Among Non-Technical BI Users](https://mlsanity.com/paper/2605.20198/) (2026) - [AI, Trust, and Teaming: The Humans-as-Handlers Approach for Autonomous and Opaque AI Systems](https://mlsanity.com/paper/2607.00523/) (2026) ## Abstract (arXiv) As AI-enhanced technologies become common in a variety of domains, there is an increasing need to define and examine the trust that users have in such technologies. Given the progress in the development of AI, a correspondingly sophisticated understanding of trust in the technology is required. This paper addresses this need by explaining the role of trust on the intention to use AI technologies. Study 1 examined the role of trust in the use of AI voice assistants based on survey responses from college students. A path analysis confirmed that trust had a significant effect on the intention to use AI, which operated through perceived usefulness and participants' attitude toward voice assistants. In study 2, using data from a representative sample of the U.S. population, different dimensions of trust were examined using exploratory factor analysis, which yielded two dimensions: human-like trust and functionality trust. The results of the path analyses from Study 1 were replicated in Study 2, confirming the indirect effect of trust and the effects of perceived usefulness, ease of use, and attitude on intention to use. Further, both dimensions of trust shared a similar pattern of effects within the model, with functionality-related trust exhibiting a greater total impact on usage intention than human-like trust. Overall, the role of trust in the acceptance of AI technologies was significant across both studies. This research contributes to the advancement and application of the TAM in AI-related applications and offers a multidimensional measure of trust that can be utilized in the future study of trustworthy AI.